Model Predictive Control of 5L-ANPC Converters with Level-Shifted Pulse-Width-Modulation
Bibliographic record
Abstract
Model predictive control (MPC) is a promising control strategy for five-level active-neutral-point-clamped (5L-ANPC) converters due to its fast dynamic performance and intuitive implementation process. However, due to a large number of the switching states of the 5L-ANPC converter, classical MPC of 5L-ANPC converters experiences a heavy computational burden. Moreover, its steady-state performance is not satisfactory due to the lack of modulators. This paper investigated the inherent feature of the level-shifted pulse-width-modulation (LS-PWM) and integration of the LS-PWM to the MPC. The resulting MPC with LS-PWM scheme provides comparative performance to that of a linear controller with LS-PWM. The computational burden can be reduced using the hexagon selection. Experimental results show that the good steady-state performance of the proposed MPC method while retaining the fast dynamic of MPC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".