A Fault-Diagnosis and Fault-Tolerant Control Scheme for Flying Capacitor Multilevel Inverters
Bibliographic record
Abstract
The high number of power semiconductors in multilevel converters makes them susceptible to failure. Therefore, one of the main concerns in utilizing multilevel inverters is their reliability. This paper proposes a new simple fault-diagnosis and fault-handling method to increase the robustness and reliability of a flying capacitor multilevel inverter (FCMLI) which is one of the most prominent multilevel inverters. The proposed method is capable of diagnosing failed switch(es) and reconfiguring the switching sequence such that the output voltage is maintained similar to a normal operation condition. The proposed scheme identifies failed switch(es) by using the information about the charging state of the capacitors and the applied switching sequence. After identifying the failed switch(es), the algorithm bypasses the failed switch(es) and converts the control signals of the faulty leg from an M -cell L-level configuration to an (M-F)-cell L -level configuration (where F is the number of failed switches). The most attractive feature of the proposed control scheme is that any number of failed switches can be tolerated, as long as the number of functional switches is higher than the minimum number of cells required to build a full-binary L-level FCMLI. Simulation and experimental results are presented that verify the effectiveness of the proposed method.
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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) | 0.001 | 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".