Simulasi Perbaikan Tegangan menggunakan Aplikasi ETAP pada Mobile Substation 150/20 KV Sistem Kelistrikan PLN (Persero) Rayon Menggala
Bibliographic record
Abstract
Drop tegangan di wilayah Tulang Bawang dan Mesuji sudah mencapai level – 50 percents dari tegangan nominal. Sedangkan berdasarkan SPLN T6.001 tahun 2013 tentang Tegangan Tegangan Standar disebutkan bahwa tegangan tertinggi dan tegangan terendah perbedaannya tidak boleh lebih besar kurang lebih 10 percents dari tegangan nominal sistem. Drop tegangan terjadi karena panjang penghantar jaringan terlalu panjang dan beban feeder besar di ujung jaringan serta terlambatnya pembangunan gardu induk Mesuji. Perbaikan tegangan pada sistem kelistrikan di wilayah Tulang Bawang dan Mesuji disimulasikan menggunakan Aplikasi ETAP dengan memanfaatkan fungsi Load Flow Analisis sehingga diperoleh hasil aliran daya pada sistem kelistrikan tersebut. Simulasi dilakukan pada dua kondisi yaitu kondisi sebelum dan kondisi setelah Mobile Substation 150/20 kV beroperasi pada sistem kelistrikan Tulang Bawang dan Mesuji. Dari hasil simulasi dengan Aplikasi ETAP dapat diketahui perbaikan tegangan pada sistem kelistrikan Tulang Bawang dan Mesuji. Sehingga apabila hasil simulasi dianggap layak, maka pengoperasian Mobile Substation 150/20 kV dapat dilaksanakan
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".