Proportional Integral Finite Set Model Predictive Control for a Transformer-Less Compact Multilevel Active Power Filter
Bibliographic record
Abstract
This paper investigates the performance of an advanced nonlinear controller that is applied to a grid connected Multilevel Active Power Filter (MAPF) with the aim of suppressing harmonics generated by a nonlinear load. The MAPF is designed based on Modified Packed U-Cell topology to boost voltage amplitude such that conventional coupling transformer is removed and the dc-link capacitors size is reduced. By providing five-level voltage using MPUC, undesired harmonics are suppressed so that THD level and the inductance size in grid filter are minimized. Balancing the capacitors voltage and tracking the desired grid reference current are the main control objective which have been guaranteed by the proposed Proportional Integral Finite Set Model Predictive Control (PI-FSMPC) technique. In this method, PI generates the current reference based on the capacitors voltage error and FSMPC controls MAPF using the current reference. Experiments and Simulations provided in this paper confirm feasibility and reliability of PI-FSMPC to attain all the control objectives.
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 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 | 0.001 |
| 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 | 0.001 | 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".