Control of AC-DC converters under unbalanced operating conditions using the DC space vector control concept
Bibliographic record
Abstract
AC-DC power converters within an unbalanced system experience abnormal harmonics on both AC and DC sides. In particular, a 2ndharmonic power ripple appears across the converter, which can have negative consequences on the converter and the load. A passive alternative to decrease these detrimental effects is to increase input and output filters of the converter, which increases cost and size while tending to decrease converter lifetime. Due to these reasons, development of control methods to mitigate the low order harmonics has received much attention. Recently, this research has been focused on control methods using feedforward schemes to nullify the ripple power. The lack of robustness in feedforward schemes, combined with the complexity of associated filters needed for sequence component extraction have been factors against their widespread deployment. This paper introduces a new controller that addresses ripple power via DC voltage feedback. A new DC space vector controller is proposed that operates together with a stationary frame AC current controller to eliminate 2ndharmonic DC ripple and mitigate the propagation of associated 3rdharmonic AC line currents. The efficacy of the controller is proven by experimental 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".