Single-Input Space Vector Based Control System for Ripple Mitigation on Single-Phase Converters<sup>1</sup>
Bibliographic record
Abstract
Applications such as renewable energy generation, electric vehicles, and low-power UPS require single-phase ac/dc conversion. However, this conversion introduces a considerable amount of second-harmonic ripple on the dc link. If not filtered, this distortion hinders the converter's performance as well as the energy quality on both the ac and dc side. To mitigate this problem, a large electrolytic capacitor is usually the solution of choice, which mitigates the voltage ripple, but has drawbacks of its own, including the increased size and cost associated with the large capacitance and the limited lifespan of electrolytic capacitors. Alternative solutions to the problem include integration of an active filter circuit to the converter, which can utilize a storage element with the objective of mitigating power ripple. Such solutions have often been proposed alongside a control system, which is either highly complex or relies on open-loop feedforward techniques. This paper presents a control system, which adapts the single-input space vector concept for a single-phase application and leverages its simplicity and closed-loop architecture, allowing the controller to perform well in the presence of disturbances and parameter uncertainty. Experimental results are provided to elucidate the controller's performance on a single-phase grid-connected photovoltaic array application.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".