Capacitor aging detection for the DC filters in the power electronic converters using ANFIS algorithm
Bibliographic record
Abstract
DC filters are responsible for more than half of the failures in the power electronic converters. One approach to improving the reliability and maintainability of the converters is to include failure diagnosis for the DC filters within the power converter. DC filters failures may be classified as sudden faults which may take the form of breakdown faults resulting from a blown capacitor fuse and gradual faults caused by capacitor aging. This paper presents a fault detection and location for the capacitor aging faults in the DC filters of the power converters. The proposed fault diagnosis is based on the adaptive neuro-fuzzy inference system (ANFIS) algorithm. The inputs to the ANFIS unit are only the input voltage of the converter as well as the voltages across the DC filters. The output of the ANFIS unit is utilized as an index in order to identify the capacitor aging fault in the power converter. Then, it locates the fault within the two DC filters installed in the power converter.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".