Intelligent Harmonic Suppressor by Adaptive Fuzzy Controller for PEC9 Inverter under Unknown Nonlinear Loads
Bibliographic record
Abstract
The presence of nonlinear loads challenges the power quality regulation in stand-alone and grid-connected converters. Employing bulky passive or active filters is common to filter out the harmonics; but this is a costly solution. In this paper, an Intelligent Harmonic Suppressor (IHS) using fuzzy control theory and Second Order Band Pass Filter (SOBPF) is proposed to mitigate the harmonics caused by unknown nonlinear loads in a stand-alone nine-level Packed E-Cell (PEC9) inverter. PEC9 is a promising multilevel topology which can generate a quasi-sinusoidal voltage waveform only by a single dc source. In the proposed IHS technique, PEC9 is forced by the fuzzy controller to track the desired current reference that is provided by an adaptive control law based on the load impedance. Simulation results verify that since the undesired harmonics emerge in the measured signals applied to the control loop, using SOBPF before the fuzzy control inputs notably reduces THD while the references for the load current and the auxiliary dc capacitors voltages are accurately tracked.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".