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.
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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) | 0.003 | 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".