Quasi-Online Low-Frequency Impedance Monitoring Scheme for Submodule Capacitors in Modular Multilevel Converters
Bibliographic record
Abstract
Despite being the most fragile component, aluminum electrolytic capacitors (AECs) are preferred in modular multilevel converters (MMC) due to their high volumetric efficiency and low price. Due to the ageing of AECs, its capacitance decreases and equivalent series resistance (ESR) increases. This can result in an increased voltage ripple, power loss, and could damage the operation of the MMC. Therefore, condition monitoring of submodule (SM) capacitors in an MMC is indispensable to enhance system reliability. This paper presents a new condition monitoring technique for SM capacitors based on the measurement of its low-frequency impedance. The low-frequency impedance is evaluated based on the information of the second harmonic component in the SM capacitor voltage and current. In this scheme, a low-frequency voltage signal is injected in the control loop of the MMC with available hardware and controller for converter control. The proposed scheme has been validated through PLECS simulations on a three-phase half-bridge submodule (HB-SM) based MMC. This technique is also verified experimentally on a single-phase MMC with a four HB-SMs per arm.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".