Control Design of a PFC With Harmonic Mitigation Function for Small Hybrid AC/DC Buildings
Bibliographic record
Abstract
Unprecedented expansion of native dc powered equipment (LEDs, computers, and consumer electronics) has increased commercial and residential dc electricity usage over the past decade. Thus, it is foreseeable that hybrid ac/dc buildings featuring both ac and dc infrastructures will coexist. A hybrid ac/dc building will involve an efficient centralized rectifier that supplies all the dc loads, while legacy ac loads will remain connected to the existing ac infrastructure. This paper explores the opportunity of harmonic mitigation at distribution level in small hybrid ac/dc building by using a centralized power factor corrector (PFC) with large bandwidth. The current reference generator for the harmonic mitigation function (HMF) is explained along with power considerations. The PFC uses a proportional resonant controller, instead of a PI controller, without requiring additional sensors in the rectifier. A computationally inexpensive implementation of the phase-locked loop is also proposed along with considerations on parameter selection. The proposals provide all the steps for the straightforward control design of the PFC+HMF with fast calculations. The HMF requires only software modifications in the PFC and one sensor to measure the nonlinear load. Simulation and experiments validate the proposed procedures.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".