Constraint models of voltage fluctuation limit on OLTC/SVR caused by DG power fluctuation and generator disconnection to assess their impacts on DG penetration limit
Bibliographic record
Abstract
With the rapid growth of distributed generation (DG) in distribution systems, DG penetration limit is becoming a more emergent issue. The short‐term voltage fluctuation resulting from the DG power fluctuation of intermittent DG sources, e.g. photovoltaic (PV) and wind power, could trigger excessive actions of a transformer with on‐load tap changer (OLTC) or a feeder step‐voltage regulator (SVR). In addition, DG tripping from feeder will cause sudden large voltage step change. These facts limit the maximum allowable penetration of DG in distribution networks. This study proposes the constraint models of voltage fluctuation limit on OLTC/SVR resulting from DG power fluctuation and generator disconnection to assess their impacts on the DG penetration limit. A simple optimal power flow methodology is built according to the practical criteria and applied to the practical feeder systems, to demonstrate the huge advantages of the proposed constraint models in the assessment of DG penetration limit, which also validates that both factors have significant impacts on DG hosting capacity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".