A Resilient Framework for Fault-Tolerant Operation of Modular Multilevel Converters
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it