PENGGUNAAN KAPASITOR BANK UNTUK MEMPERBAIKI FAKTOR DAYA DAN MENGURANGI RUGI-RUGI DAYA MENGGUNAKAN FUZZY LOGIC CONTROLLER DI QUEST HOTEL KUTA BADUNG
Bibliographic record
Abstract
Quest Hotel Kuta is a place of business engaged in lodging services. To support operationalneeds, the hotel uses electrical equipment, most of which are inductive. The use of high inductiveloads can produce high reactive power, depending on the power factor because the active powerobtained from the installed power will be less. Based on the measurement results at Quest Hotel Kutathe measured power factor is 0.70, while PLN charges the excess cost of kVARH to the customer ifthe average power factor is less than 0.85. To be able to improve the low power factor can be done byusing a capacitor bank, the capacitor uses the reactive power required at the inductive load. Changingthe power factor in consumers can change. To equalize the power change factor, the capacitor bank isassembled to work multiple steps / multi steps. Multi-step capacitor banks consist of several capacitorswith the same or different capacities. Based on the results of measurements before using the fuzzylogic method, the obtained power factor is 0.70 to 0.74 while the capacitors are installed from 12.5kVAR to 25 kVAR. After using the fuzzy logic controller method the power factor obtained is 0.80 to0.87 and capacitors using the 13.1 kVAR to 22.4 kVAR method. Whereas the power losses beforeusing the fuzzy logic controller method are 26220.5 watts. After using the fuzzy logic controllermethod, the power losses incurred were 23214.45 watts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".