Intelligent Performance Assessment of Multilevel Converter-Based Active Filters in PV-Integrated Smart Grids
Bibliographic record
Abstract
The rapid integration of photovoltaic (PV) systems into modern power grids has intensified challenges in power quality, particularly harmonic distortion, reactive power imbalance, and stability under fluctuating solar and load conditions. Shunt Active Power Filters (SAPFs) are a proven solution, yet their performance strongly depends on converter topology. This paper presents an intelligent performance assessment of two-level (2 L) and three-level (3 L) converter-based SAPFs for PV-integrated smart grids. A complete MATLAB/Simulink model is developed, incorporating MPPT-based PV generation, nonlinear loads, and dynamic test cases of irradiance variation and unbalanced conditions. Results demonstrate that while the 2L SAPF improves power quality with a source current THD of $\mathbf{1 4. 4 5 \%}$ and power factor (PF) of $\mathbf{0. 9 4}$, the $\mathbf{3 L}$ SAPF achieves far superior performance, reducing THD to 1.17% ($\approx 92 \%$ improvement), raising PF to 0.99, and enhancing dynamic response by 37%. Furthermore, under solar irradiance stepdown and unbalanced nonlinear load conditions, the 3L SAPF maintains nearly sinusoidal and balanced source currents, validating its robustness for real-world PV applications. These findings confirm that multilevel SAPFs are more suitable for medium- and high-power PV systems, offering improved efficiency, reliability, and compliance with grid standards.
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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) | 0.000 | 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) | 0.001 | 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".