Model Predictive Control for Three-Phase Three-Level NPC Inverter Based APF Interfacing Single Stage Photovoltaic System to the Grid
Bibliographic record
Abstract
A finite control set model predictive control (FCS-MPC) based controller has a fast dynamic response and robustness. furthermore, the presence of a cost function gives designers a degree of freedom to include system control targets, constraints and system non-linearities. On the other hand, Multilevel inverter (MI) topologies are becoming a strong alternative in distributed power generation system (DPGS), among these topologies is the three-phase three-level NPC (TTLNPC) inverter. Generally, to properly operate this topology, the applied current control ensures the achievement of two main objectives. First, the output current must be controlled to track its reference. Second, the two dc-link capacitor voltages have to be equal and balanced. In this paper, FCS-MPC is proposed to control the TTLNPC inverter based parallel active power filter (APF) adopted to connect a photovoltaic system (PVS) to the grid and perform a harmonic mitigation. The proposed FCS-MPC exploit the model of the system to predict the future values of the inverter currents by selecting the best voltage vector that aims to minimize a predefined cost function. Instead of using the popular redundant vectors algorithms to balance the two-split dc-link capacitor voltages, another term will be added to the expression of the cost function to achieve this goal. The PV panel is coupled directly to the inverter without DC/DC converter, the P&O MPPT algorithm is responsible to generate the capacitor reference voltage whatever the climatic conditions are. Simulations using Matlab/Simulink were performed to prove the efficiency of the proposed technique to mitigate the grid current harmonics, and to ensure a continuous power injection and perform a load power sharing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".