Concept of Best Energy Transfer Scenario and a True Expression for Power Factor
Bibliographic record
Abstract
The best energy transfer scenario (BETS) is a situation under which the optimum transfer of power (or energy) from the electric utility to an electric power consumer is achieved. Such a scenario is important because it is used as a reference point to evaluate how efficient the load operates. The index of power factor (PF) is used to quantify such an efficiency. The closer the PF to unity corresponds to more efficient performance of the load. This paper is to show that the conventional definition of the BETS is inadequate and results in an unfair value for the PF unless the transmission line is perfect and lossless (which is an unrealistic condition). The paper also investigates an alternative definition for the BETS which exists in the literature. The paper concludes that the second definition of BETS is also inadequately used and the line losses are overlooked in the existing formulations. However, the paper shows that the second BETS can be modified and properly used to calculate a fair value for the PF in realistic conditions without overlooking the impact of transmission line losses.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".