Open-Circuit Switch Fault Diagnosis and Fault- Tolerant Control for Output-Series Interleaved Boost DC–DC Converter
Bibliographic record
Abstract
This article proposes a fault-tolerant control method for an input-parallel–output-series (IPOS) converter under open-circuit switch failure, which mainly focuses on two parts: fault diagnosis (fault detection and fault identification) and remedial action. The fault diagnosis is realized based on immersion and invariant observer (I&IO), which has strong robustness to parameter uncertainty and external disturbances, and therefore, it can be designed using only the crude converter model with nominal parameters. Moreover, the sampling frequency required by the fault diagnosis is the same as the frequency required by the system controller. Thus, the fault diagnosis module can be easily embedded in the well-designed power system without extra sensors. Based on the method, the open-circuit fault in power switches can be detected and identified within two switching periods. As for remedial action, two redundant switches are needed for postfault reconfiguration. Also, the remedial action can be immediately triggered after the switch failure is detected. To reduce the complexity of remedial action, the same postfault reconfiguration will be carried out for the open-circuit failure in different switches. Besides, system controllers are also carefully designed to guarantee the performance of the postfault converter. Both simulations and experiments are conducted for the validations, and the results have shown the effectiveness, robustness, and rapidity of the proposed method.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".