A Novel Multilevel Inverter Structure for Renewable Energy Applications
Bibliographic record
Abstract
Wide band gap switches (WBGS) are the next generation of power electronic switches. Higher efficiency, higher operating switching frequencies, increased power density as well as reduced size and weight and consequently lowering overall system costs are some of WBGS advantages over their Si counterparts. Hence, employing these switches in power electronic converters seems crucial. Inverters are one of the most useful converters in power electronics with a wide range of applications. However, these converters have some drawbacks like low switching frequency limited by Si switches and low efficiency due to high sheet resistance of silicon. By taking the advantage of WBGS in inverters, issues regarding Si switches in these converters can be solved. Therefore, in this paper, a novel GaN-based inverter structure for renewable energy applications is proposed. This inverter has two power supplies, four GaN HEMTs, and two Si MOSFETs. The proposed inverter has the ability to produce up to 7 voltage levels by using two DC sources and up to 5 voltage levels by using one DC source and one capacitor. To show the advantages of the proposed inverter structure, it has been simulated using PSIM. Furthermore, different comparisons are done with similar structures to prove the effectiveness of the proposed inverter in terms of the number of components.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".