Design and Comparison of High Performance Stationary-Frame Controllers for DVR Implementation
Bibliographic record
Abstract
The performance of a dynamic voltage restorer (DVR) is determined solely by its controller. The design of high performance control algorithms for DVR control with improved robustness and desirable steady-state and transient characteristics is therefore an important area of study. In this paper, two voltage controllers are proposed in the stationary frame for DVR voltage regulation. A P+resonant controller is first designed to achieve good positive- and negative-sequence fundamental voltage control with the virtue of having high gains around plusmn50Hz. Stationary-to-synchronous frame transformations carried out in traditional synchronous proportional-integral regulators are no longer required with this method. However, with the purpose of achieving explicit robustness in face of parameter variations, an Hinfin controller is also designed. Detailed design procedure is presented to show how an Hinfin controller with high gains around plusmn50Hz can be synthesized through careful selection of its weighting functions. A thorough discussion and performance comparison of these two controllers in both transient and steady-state conditions are also carried out. Finally, both controllers are extensively tested on a laboratory 10-kV medium voltage level DVR system with various voltage sags and loading conditions
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".