Fault Detection Methods for Three-Level NPC Inverter Based on DC-Bus Electromagnetic Signatures
Bibliographic record
Abstract
This paper proposes two new open-circuit fault detection methods suitable for the diagnosis of power electronics converters. The faulty semiconductor devices are identified using conducted and radiated electromagnetic signatures of the dc bus through nonintrusive measurements. The first method uses a low-cost electromagnetic interference filter to collect the common-mode emissions signature. The second one utilizes an external antenna to collect the emitted near-field signature. Both methods are tested on a three-level neutral point clamped inverter (NPC) inverter with the aim to identify the clamping diodes open-circuit faults. Indeed, each open-circuit fault affects the common-mode emission signature in the time-domain, while the fast Fourier transform of the emitted near-field showed substantial reduction of spectrum amplitude at a specific radio frequency range and the appearance of a new spectral rail. The effectiveness of these two methods has been tested through numerical simulations and validated by experimental results which confirmed its high performance in detecting single as well as multiple open-circuit faults for three-level NPC inverter.
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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".