Health Monitoring Scheme for Submodule Capacitors in Modular Multilevel Converter Utilizing Capacitor Voltage Fluctuations
Bibliographic record
Abstract
The modular multilevel converters (MMCs) have emerged as one of the promising topologies for medium/high power applications. The aluminum electrolytic capacitors (AECs) are usually employed in the MMC as floating capacitors due to their high volumetric efficiency and low price. The AECs are reported as one of the most fragile components in the converter, which gradually deteriorates over the time due to vaporization of the electrolyte. As a result, its capacitance decreases and equivalent series resistance (ESR) increases with ageing, 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 the aged capacitors. Therefore, health monitoring of SM capacitors is essential to enhance the reliability of the MMC by predictive maintenance. This paper presents a new health monitoring technique for SM capacitors in the MMC utilizing inherent SM capacitor voltage fluctuations. The capacitance is estimated by second-harmonic impedance, which is evaluated using the ripple in capacitor voltage and current. The proposed method utilizes the available measurement used for the converter control and can be easily implemented in the same converter controller. The proposed scheme is verified for five-level MMC through simulation results in PLECS software.
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 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".