Failure Prediction of Submodule Capacitors in Modular Multilevel Converter by Monitoring the Intrinsic Capacitor Voltage Fluctuations
Bibliographic record
Abstract
Modular multilevel converters (MMCs) are emerging as a promising topology for medium- and high-power applications. Aluminum electrolytic capacitors (AECs) are usually employed in MMCs as floating capacitors due to their high volumetric efficiency and low price. AECs gradually deteriorate over time due to electrolyte vaporization, and for this reason, they have been recognized as one of the most fragile components in the converter. As they continue to age, the AEC's capacitance decreases and its equivalent series resistance increases, which can result in an increase of submodule (SM) capacitor voltage ripple, power loss and could damage the operation of the MMC with prolonged use of aged capacitors. To prevent such damage, monitoring the health of capacitors is an important step to enhance the reliability of the MMC by predictive maintenance. This paper presents a failure prediction scheme for SM capacitors in the MMC by monitoring the SM capacitor voltage oscillations. Detailed simulation studies are carried out for a five-level MMC in the PLECS® platform and verified experimentally. The estimated capacitance through simulations and experiments is in close agreement to that value measured using the LCR meter.
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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) | 0.001 | 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 | 0.000 |
| 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 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".