Design strategy for optimum rating selection in interline DVR
Bibliographic record
Abstract
This paper is concerned with design strategy for optimizing the total rating of an interline dynamic voltage restorer (IDVR). DVR has been used in distribution networks for several years as a mean to mitigate voltage sags. In designing a DVR, there is always a trade-off between the size of energy storage system and the rating of DVR. An IDVR, which is two DVRs installed in two feeders with common dc bus, has the capability of active power exchange between two DVRs, and thus the energy storage device is not an issue. Therefore, the design criteria for the selection of rating of an individual DVR are not applicable in IDVR structure. In this paper, the basic equations governing the operation of an IDVR are obtained. The important factors which cause the difference between IDVR and individual DVRs are addressed. A new step by step design procedure is proposed with the objective of optimum selection of rating for an IDVR structure. Illustrative examples are given to show the applicability of the proposed method.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".