Submodule Voltage Similarity-Based Open-Circuit Fault Diagnosis for Modular Multilevel Converters
Bibliographic record
Abstract
Fault diagnosis is indispensable for the reliable operation of modular multilevel converters (MMCs). This paper presents a submodule voltage similarity-based, real-time, and fast open-circuit fault diagnosis method for MMCs. The proposed fault detection and location (FDL) method is derived based on the similarity analysis of capacitor voltages under both normal and fault conditions. Due to the absence of the discharging current path caused by the open-circuit fault, capacitor voltage of the submodule with the faulty switch will differ from those with healthy switches. This characteristic can be extracted by designing the correlation coefficients of the capacitor voltages at the same arm. With the help of correlation coefficients, the open-circuit fault can be located at the early stage before the capacitor voltage is charged very high. All the data required for the proposed FDL method can be obtained by the available sampled data for the control scheme of MMCs. No extra measurement or hardware is required. Experimental results validate that the proposed FDL method can detect and locate the open-circuit fault rapidly and accurately within one fundamental period.
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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 | 0.001 |
| 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".