On-line diagnosis for rectifier open circuit faults and input voltage unbalance based on the output DC voltage
Bibliographic record
Abstract
Power electronics converter systems (PECS) are significant corner stones in enormous variety of industrial applications, including smart grids and variable speed drives. One of the factors that assist to improve such systems is the study of their fault mode behaviors. Besides that, the open circuit (O-C) faults in the PECS do not trigger the standard protection systems in the converter but rather cause system malfunction. Therefore, the O-C fault diagnoses become critical for PECS. This paper presents an on-line fault diagnostic algorithm for O-C faults of the power switches in the rectifier circuit, as well as the identification of the unbalance input voltage to the rectifier. The proposed techniques are based solely on the measurements of the output DC voltage of the rectifier; so, they avoid any additional sensors or hardware in comparison with the previous researches. The experimental evaluations for the proposed algorithms are implemented using a coupled DC motor with AC synchronous generator scheme, and demonstrate the effectiveness and the robustness of the proposed diagnostic algorithms in this paper.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".