Fault Diagnosis and Tolerant Operation Method of Open Circuit Fault in Modular Multilevel DC/DC Converter With Quasi-Two-Level Modulation
Bibliographic record
Abstract
The modular multilevel DC/DC converter (MMDC) is a typical high-voltage, high-power DC/DC converter, combining the advantages of quasi-two-level modulation with high DC voltage utilization and strong power transmission capability, having broad application prospects in DC grids. The MMDC is composed of numerous insulated gate bipolar transistor (IGBT)- based switches which may impose an open-circuit fault (OCF). When an OCF occurs in the switch of the MMDC, the internal voltage and current may severely distort, potentially affecting the safe operation of the system. To address this issue, this paper proposes a quasi-two-level modulation based OCF diagnosis and fault-tolerant operation method for an MMDC, which utilizes the signal synthesis of arm current and arm voltage modulation. First, the relationship between fault arm current and modulation wave is revealed. Based on this, a fault diagnosis algorithm is proposed to accurately identify the faulty arm and the fault type, without requiring additional sensors or complex calculations. The MMDC is then controlled in the proposed fault-tolerant mode by actively modifying the modulation wave until the specific faulty submodule is located and bypassed. This reduces voltage and current distortion. Subsequently, the modulation wave automatically returns to normal, allowing the system to quickly recover. Verification results demonstrate that the proposed method accurately and rapidly diagnoses the OCF and achieve faulttolerant operation.
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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 | 0.000 |
| 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) | 0.001 | 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".