Comparison Using Express Feeder and Capacitor Bank Allocation to Corrective Voltage Level on Primary Distribution Feeder
Bibliographic record
Abstract
This study aimed to: examine the total power loss of the primary distribution system and the impact of the capacitor bank and express feeder allocation to corrective voltage level on the primary distribution system. Therefore, a case study was taken in the primary distribution of the PLTD (diesel generation) Kelapa Lima Merauke then using Electrical Transient and Analysis Program (ETAP) simulation models to make power flow analyzing. The datas needed is one-line diagram, nominal voltage, generator rating, bus, transformer and transmission / distribution. The results obtained are normal loading power losses of 0.014 MW, voltage level for Feeder of Kota Satu was still stable because in cover range of voltage drop 5 %. The other side, corrective voltage level for Feeder of Merkuri from 17.65 kV to 17.75 kV after capasitor bank in amount of 134 kVAr allocated, drop voltage decreased from 13.31 % to 11.25 %, but it still unstable because out of cover range of voltage drop. For that, after using express feeder can be correct voltage level for feeder of Merkuri from 17.65 kV to 19.39 kV or drop voltage decreased from 13.31 % to 3.05 %. Therefore, using express feeder on Merkuri feeder better than capacitor bank allocation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".