A New Active Front-End Control for Regenerative Cascaded H-Bridge Motor Drives With Filter- Less Interfacing Capability
Bibliographic record
Abstract
This article proposes a new active-front-end (AFE) control method for regenerative cascaded H-bridge (CHB) motor drives. Conventional CHB converters have dominated the market for the medium-voltage industrial motor drives. However, conventional CHB converters cannot provide regenerative capability. To enable regeneration; diode-front-ends in power cells are replaced with insulated-gate bipolar transistor (IGBT)-based pulsewidth modulation (PWM) AFEs. Despite the appealing dynamic performance of PWM AFEs, their integration to the CHB converters is not optimal. First, they introduce more semiconductor losses due to the high-frequency switching. Second, they introduce switching harmonics that are not cancelled by phase-shifting transformers. Therefore, they require additional harmonic filtering solutions to comply with grid harmonic standards. To resolve these challenges, this article proposes a new AFE control for regenerative CHB converters based on fundamental frequency switching (FFE) to reduce switching frequency and thus power losses. The operation of FFEs at nominal and sag voltage conditions is presented, in addition to the capability of filter-less interfacing to the transformer. Finally, the performance of the proposed control is validated experimentally on a seven-level regenerative CHB drive.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".