Single- and Multiswitch Fault-Tolerant Inverter Topology With Preserved Output Power and Extreme Learning Machine Fault Detector
Bibliographic record
Abstract
Open-circuit and short-circuit faults in switches affect the availability of inverters for high-reliability applications such as military, aerospace, and industrial systems, where a continuous supply of power is crucial. In this article, a five-level inverter structure is proposed that can handle both open-circuit and short-circuit faults for single- and multiswitches. In both healthy and faulty operations, the proposed inverter maintains the rated output power and efficiency. In addition to being a reliable and appropriate candidate for emergency loads, the proposed fault-tolerant inverter topology (FTIT) has several promising features, such as a low number of employed devices, a higher efficiency, and a lower total standing voltage. To demonstrate the superior performance of the proposed topology as compared to state-of-the-art structures, comprehensive comparisons have been made in terms of quantitative comparisons, efficiency, and cost. Moreover, this article performs a detailed reliability analysis to evaluate the proposed FTIT and compares its performance with that of other FTITs. Furthermore, a fault detection method based on an extreme learning machine is proposed for detecting the healthy and faulty operation of the proposed FTIT. Simulation and experimental results are presented for different faulty cases to verify the feasibility of the proposed fault-tolerant inverter topology.
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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 | 0.000 |
| 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 | 0.001 |
| 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".