DC-Link Voltage Ripple Control of Regenerative CHB Drives for Capacitance Reduction
Bibliographic record
Abstract
The diode-front-end cascaded H-bridge (CHB) inverters have prevailed in the nonregenerative industry drive domain for high-power medium-voltage applications. The regenerative version of the CHB drives is made possible by adding the extra active-front-end rectifier in each power cell, such as a three-phase PWM rectifier. However, due to the instantaneous power unbalance, the dc-link capacitors of the regenerative power cell need to be overdesigned to maintain a stable low ripple dc-link voltage. To reduce the dc-link capacitance, this article proposes a novel closed-loop voltage ripple controller for the regenerative CHB drive without adding extra sensors. In the proposed method, dc-link voltage ripple amplitude and phase angle are accurately detected with a high-performance adaptive filter. Moreover, a latent instability issue is discussed and is avoided in the proposed controller. The performance of the proposed control strategy is validated experimentally on a seven-level regenerative CHB drive.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".