Direct Predictive Control for a Nine-Level Packed E-Cell (PEC9) Converter Based Shunt Active Power Filter (SAPF)
Bibliographic record
Abstract
This paper presents a direct non-linear based predictive control for a single-phase Shunt Active Power Filter nine-level Packed E-Cell inverter (SAPF-PEC9). The proposed control based on a Finite Set Model Predictive Control (FS-MPC) has been designed to compensate the reactive power requested by the nonlinear load at the Point of Common Coupling (PCC). An outer loop consisting of a PI regulator together with a low pass filter is used to regulate the main capacitor voltage and to inject the proper filter reference current. Meanwhile, the overall design is very simple where a very fast and robust controller is achieved. Moreover the nine-level converter operation together with the direct predictive approach allows the reduction of the passive filter at the shunt converter terminals. Hence a microscale filter is obtained. The performance of the proposed hybrid controller in reactive power compensation, harmonics suppression and unity power factor operation is investigated under stiff grid conditions (zero impedance) through both normal and dynamic load-change operation. Simulation results are provided to validate the fast and effective dynamic performance, the very low harmonic content in the mains current as well as the DC capacitors balancing of the PEC inverter.
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 | 0.000 |
| 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 | 0.001 | 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".