Enhanced Sampled-Data Models for Multistage Predictive Current Control of Four-Level Inverters
Bibliographic record
Abstract
The use of forward Euler-based sampled-data models in multi-stage predictive current control (MS-PCC) for multilevel inverters (MLIs) results in a poor prediction accuracy with the rise in sampling time. These models also lead to higher harmonic distortion and capacitor voltage ripple in MLIs. To address these problems, modified Euler-based models are proposed for an MS-PCC, and they are applied to a four-level MLI. Also, the proposed MS-PCC is formulated to reduce the common-mode voltage indirectly, thereby eliminating the need for pre-selection of voltage vectors and weighting factors. The performance of MS-PCC with the proposed modified Euler and the existing Euler-based models is investigated experimentally on a dSPACE-controlled laboratory prototype under identical operating scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".