Application of generalized peak current controllers for active power filters and rectifiers with power factor correction
Bibliographic record
Abstract
Fast transient response, inherent current limiting, and simple implementation are some of the advantages of peak current controllers. Such features makes these controllers suitable for a variety of power converters such as inverters and dc-dc converters. The switching frequency of peak current controllers, however, is not constant and is susceptible to the operating point and system parameters. Recently, a generalized peak current controller (GPCC) is proposed that addresses the variable switching frequency of conventional peak current controllers. The GPCC formulation is derived such that it can be applied to various inverter topologies. In its original format, the GPCC can only regulate sinusoidal currents in inverter applications. In this paper, a simple yet effective extension of the GPCC is proposed to cover two more applications: (i) inverters as active power filters and (ii) boost rectifiers with power factor correction. The proposed extended methods still maintain the simplicity and fixed switching frequency of the GPCC. After reviewing the concept of the original GPCC, the proposed controllers are elaborated and simulations and experimental results are provided to verify their 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".