Self Tuning Filter Based Fuzzy Logic Controller for Active Power Filter
Bibliographic record
Abstract
The performance of the Active Power Filter (APF) depends on the identification strategy of the reference currents and their control. Among the control strategies proposed in the literature to identifying the reference currents, that based on Self Tuning Filter (STF) which allows extracting directly the voltage and current fundamental components in the α-β axis at high performances, without any Phase Locked Loop (PLL). The performance of STF is function of its proportional parameter. However, there is no technique existed in the literature to dimensioning the proportional parameter of the STF filter. This paper presents an improved method for identifying the reference harmonic currents to be generated by the APF, based on STF for the extraction of harmonic currents and equipped with a Fuzzy Logic Corrector (FLC) in order to improve the waveform of the electric lines currents. The FLC adjust in real time the proportional parameter of the STF filter. The proposed FLC-STF regulator allows to having an optimal extraction of the harmonic currents. The conventional hysteresis method was used for the current control technique to generate the switching sequences of the static switches of the APF voltage inverter. The obtained simulation results, performed under the MATLAB/Simulink® environment on a system feeding a non-linear load, show good performance.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".