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Fault-Tolerant Control of Full-Bridge Mmc Operating in Boost Mode for Mvdc Applications

2025· article· en· W4416728367 on OpenAlexfundno aff
Davide D’Amato, Nidhi Bisht, Marco Liserre

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsnot available
FundersSchleswig-HolsteinMinistère de l'Éducation, du Loisir et du Sport Québec
KeywordsOvermodulationFault (geology)Modular designReliability (semiconductor)VoltageCapacitorFault toleranceMode (computer interface)

Abstract

fetched live from OpenAlex

To enable safer and more resilient operation, a full-bridge based modular multilevel converter (FB-MMC) has been proposed for medium voltage direct current (MVDC) applications, owing to its inherent dc-side fault blocking capability. In addition, FB-MMC enables boost mode operation, which allows flexible control in the overmodulation region and facilitates step-down of the de-side voltage. However, the increased number of switching devices in the FB-MMC reduces the reliability of the converter. While previous studies have focused on the external fault management of FB-MMCs, limited attention has been given to internal fault tolerant operation, revealing a critical gap in the existing literature. This paper proposes a fault-tolerant operation for FB-MMC which prevents submodule capacitor voltage rise during an internal fault. Furthermore, the proposed fault tolerant method can maintain the post-fault performance of the FB-MMC same as pre-fault conditions without increasing the overall cost of the MMC by considering any redundant submodules. The present article also explains the relationship between the multiple submodule fault tolerant capability and the overmodulation region of the FB-MMC. The performance of the technique is validated through simulations and experimental tests with the 9-level FB-MMC prototype.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.965
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.259
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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