A Novel 23-Level Inverter Topology with Symmetric-Asymmetric Hybrid Modules and Cross-Switching
Bibliographic record
Abstract
This paper proposes a reduced switch 23-level cross-switch multilevel inverter that integrates symmetric and asymmetric submodules to deliver high-quality output with the reduced hardware complexity. The topology uses only four isolated DC voltage sources, and nine unidirectional switches arranged in a cross-switching structure, eliminating the need for bidirectional switches and H-bridge inverters. Compared to conventional topologies, the proposed structure minimizes the voltage stress and the total number of switches, enhancing reliability, efficiency and thermal performance. MATLAB/ Simulink simulations validate the effectiveness of the proposed inverter with three load types (purely resistive, resistive and inductive, and purely inductive load) under five loading conditions with and without a LCL filter. Results demonstrate excellent output voltage quality with a total harmonic distortion (THD) of $3.60 \%$ for a resistive load, and $1.13 \%$ for an inductive load with a compact LCL filter. The proposed inverter topology meets IEEE Std. 519, and offers a promising solution for costeffective, high-performance medium-voltage power conversion applications.
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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) | 0.005 | 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".