A Resilient Framework for Fault-Tolerant Operation of Modular Multilevel Converters
Bibliographic record
Abstract
This paper presents a resilient framework for fault-tolerant operation in modular multilevel converters (MMCs) to facilitate normal operation under internal and external fault conditions. This framework is realized by designing and implementing a supervisory algorithm and a postfault restoration scheme. The supervisory algorithm includes monitoring and decision-making units to detect and identify faults by analyzing the circulating current and submodule capacitor voltages in a very short time. The postfault restoration scheme is proposed to immediately replace the faulty submodule with the redundant healthy one. The restoration is achieved by virtue of a multilevel modular capacitor-clamped dc/dc converter (MMCCC), which is redundantly aggregated to each arm of the MMC. This design effectively guarantees smooth mode transition and handles the failure of multiple submodules in a short time interval. In addition, a modified modulation scheme is presented to ensure submodule capacitor voltage balancing of the MMC without implementing any additional hardware. Fast fault identification, a fully modular structure, and robust postfault restoration are the main features of the proposed framework. Digital time-domain simulation studies are conducted on a 21-level MMC to confirm the effectiveness and resilience of the proposed fault-tolerant framework during internal and external faults. Furthermore, the proposed framework is implemented in the FPGA-based RT-LAB real-time simulator platform to validate its resilience in a hardware-in-the-loop setup.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".