Five-Level Reduced-Switch-Count Boost PFC Rectifier with Intelligent Controller
Bibliographic record
Abstract
A five-level Power Factor Correction incremental rectifier (PFC) is proposed in this paper. In this topology, the output voltage and current of the rectifier are controlled using the multilevel modulation and smart controller technologies. A multi-carrier pulse width modulation is used to create the switching pulse. In this topology, the number of semiconductor switches is reduced to 3. The smart controller is implemented using a Multilayer Perceptron (MLP) neural network and it is trained using the backpropagation algorithm. This controller is used instead of the well-known PID controller to control the input voltage and current. It should be noted that in this work, the goal is to design an intelligent controller using a neural network instead of a PID controller. The results obtained using this controller as compared to the PID controller show a decrease in the peak voltage, an increase in the rise time, and a ripple reduction in the output voltage. This study is conducted using the Simulink environment in MATLAB and the results suggest that a smart controller can be an alternative to the PID controller.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".