A New Fault-Tolerant Technique Based on Nonsymmetrical Selective Harmonic Elimination for Cascaded H-Bridge Motor Drives
Bibliographic record
Abstract
This article proposes a new fault-tolerant technique to increase the maximum balanced line-to-line output voltage of the cascaded H-bridge (CHB) motor drives. The CHB converters have been widely used for medium-voltage motor drives due to their scalability and reliability features. A significant indicator of the reliability is the maximum balanced line-to-line voltage amplitude under fault conditions. This article adopts a nonsymmetrical selective harmonic elimination (SHE) formulation to further extend the output voltage range with a good harmonic profile under fault conditions. The dc current component can be regulated for the dynamic braking operation. Based on the nonsymmetrical SHE formulation, the fault-tolerant problem that achieves the maximum output voltage range and good harmonic profile is converted to an optimization problem, which can be solved by the proposed optimization framework. By properly selecting the output voltage waveforms, the entire converter voltage capability can be achieved under fault conditions with a good harmonic profile. The performance of the proposed method is evaluated experimentally on a seven-level CHB motor 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 | 0.001 | 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".