A New Approach to Input Filter Design for Regenerative Cascaded H-Bridge Drives
Bibliographic record
Abstract
The diode-front-end (DFE) cascaded H-bridge (CHB) inverter has prevailed in the nonregenerative industry drive domain for high power medium voltage applications due to its modularity, scalability, and fault-tolerant capability. The regenerative version of the CHB drives is made possible by adding the extra active-front-end (AFE) rectifier in each power cell, such as a three-phase Pulse width modulation (PWM) rectifier. However, AFE introduces the switching harmonics which requires to be attenuated by designing a grid interfaced filter to comply with the harmonic standard IEEE 519-2014. High-order filters likeLCLfilters are not preferred due to complex inherent resonances in a multiparallel AFE system. A large number of capacitive components in the system rouse multiple resonances, which decreases system reliability and modularity. This article proposes an active filtering strategy based on the optimal asymmetric carrier-shifting method for regenerative CHB drives with onlyLfilters. The required filter size is reduced significantly while complying with IEEE 519-2014 standard and thus improves the overall size, cost, and efficiency. The proposed filtering strategy is validated experimentally using 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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".