Detecting and Localizing Open-Circuit Switch Faults in MMCs Using a Model Informed Estimation Scheme With Low Computational Complexity
Bibliographic record
Abstract
The detection and localization of switch open-circuit faults (OCFs) in modular multilevel converters (MMCs) is crucial for enhancing their reliability. This article presents a model informed estimation-based fault detection and localization (FDL) scheme with low computational burden and implementation complexity. Its main novelty comprises two parts: derivation of a new model that quantifies the expected deviation in submodule capacitor voltages due to OCFs, and utilization of a Disturbance Observer (DOB) that, by leveraging the derived model, needs only one signature waveform for each arm. As a result, the proposed FDL scheme enables estimation of OCFs while maintaining very low and constant computational burden and implementation complexity regardless of the number of installed submodules per arm. To the best of the authors' knowledge, this work is the first to explore the use of OCFs models that can quantify the OCFs-induced deviations in the MMC capacitor voltages. Experimental results verify that the proposed model-informed FDL scheme with DOB can detect and localize OCFs accurately and rapidly, while retaining important traits such as robustness to load changes and measurement noise.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".