Model Predictive Approach for a Simple and Effective Load Voltage Control of Four-Leg Inverter With an Output <inline-formula> <tex-math notation="TeX">$LC$</tex-math></inline-formula> Filter
Bibliographic record
Abstract
This paper presents a finite control set model predictive strategy and its application to the load voltage control of two-level four-leg inverters. The proposed approach uses the novel discrete-time model of the inverter and output LC filter in order to predict the variables to be controlled. These predictions are carried out for the 16 switching states of the inverter and are evaluated using a cost function. The switching state that forces the load voltages to be closest to their respective references is chosen and applied to the inverter. The behavior of the predictive controller has been investigated, and the changes to both inductive and capacitive filter parameters have been considered. In order to improve the reliability of the fourth leg as well as the overall inverter efficiency, a solution is proposed, which combines hardware and software reconfigurations. The feasibility of the proposed method is verified through simulation and experimental results considering single-/three-phase, balanced/unbalanced, and linear/nonlinear loads.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".