A D-Q rotating frame DC-bus voltage controller for bi-directional single-phase AC/DC converters
Bibliographic record
Abstract
This paper presents a closed-loop control technique in D-Q rotating frame for bi-directional single-phase AC/DC converters. The proposed control system allows the active and reactive power control in both directions from grid to vehicle and vehicle to grid, while controlling the DC-bus voltage. Due to the lack of freedom degrees required for orthogonal systems, using the D-Q frame is a challenge in single-phase power conditioning systems. In this paper, an all-pass filter has been used to generate the orthogonal signals in the closed-loop control system. This method generates the orthogonal signals without degrading the dynamics of the closed-loop control system and with less noise in comparison to the other common methods such as differentiators. A DC-bus voltage estimator based on adaptive filter has also been used in this paper to remove the double frequency ripple and in turn increase the reliability and speed of the proposed controller. The proposed control scheme is used to implement the smart charging and power management strategies of the residential loads using electric vehicles as a storage system. Simulation and experimental results demonstrate the transient and steady-state performance of the proposed controller in different modes of operation. Also the performance of the proposed DC-bus voltage estimator in removing the double frequency ripple as well as the application of the proposed control scheme in implementation of power management strategies in a typical household, has been shown by using simulation results.
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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 | 0.001 |
| 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".