Unified Switching Frequency Minimized Harmonic Mitigation Technique for Asymmetric Cascaded H-Bridge Converters
Bibliographic record
Abstract
To obtain the optimal switching pattern with low-order harmonic mitigation capability and the optimal switching angle distribution in asymmetric cascaded H-bridge (ACHB) converters with the lowest switching frequency, a unified switching frequency minimized harmonic mitigation (SFMHM) model for ACHB converters is proposed in this article. Based on PWM waveform discretization, the switching frequency of each H-bridge cell can be mathematically modeled as the state change of output levels. Then, the overall switching frequency of all cells is used as the objective function to be minimized in the proposed model, while the amplitude of fundamental and low-order harmonic components to be regulated are used as constraints. To achieve an optimal switching angle distribution with fewer switching losses, the well-designed weighting factors are assigned to the switching frequency functions of different cells in the proposed formulation. The model-solving algorithm based on mixed integer programming is introduced, and various numerical results are presented and compared with the conventional selective harmonic elimination PWM (SHE-PWM) and selective harmonic mitigation PWM (SHM-PWM) for ACHB converters. Simulation and experimental results verify the effectiveness of the proposed model.
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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".