Switch Open-Circuit Fault Detection and Localization for Modular Multilevel Converters Based on Signal Synthesis
Bibliographic record
Abstract
The open-circuit fault detection and localization (FDL) scheme plays a significant role in improving the reliability of modular multilevel converters (MMCs). This article presents a simplified signal synthesis-based FDL scheme with good generality and robustness. The proposed FDL scheme exploits the characteristics that the capacitor voltages of faulty submodules (SMs) have different patterns and higher values than those of healthy ones. Based on this knowledge, certain signature voltage signals are selected, and waveforms consisting of such signatures are reconstructed by the signal synthesis technique to detect and localize the fault. The proposed scheme is suitable for both low- and high-voltage MMCs since the computational complexity does not change with the number of SMs. The scheme does not rely on system parameters, which makes it immune to parameter uncertainties, and there is no need for any additional sensors. Both single and simultaneous multiple faults can be handled with the proposed scheme. Simulation and experimental results validate that the proposed approach based on the signal synthesis method can effectively detect and localize the fault accurately under different loading conditions and has robustness against load change and circuit parameter uncertainties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".