A new fuzzy based Representative Quality Power Factor for nonsinusoidal situations
Bibliographic record
Abstract
In sinusoidal situations, power factor definition is unique and expressive. However in nonsinusoidal situations and/or nonlinear load different power factors are proposed to express these situations. In this paper a new fuzzy based representative quality power factor is introduced to represent these power factors. The proposed representative quality power factor (RQPF) was applied to different cases, linear, nonlinear, sinusoidal, nonsinusoidal considering lagging and leading power factor. It is shown that the new RQPF is expressive and accurately represents the existing power factors in all cases and in all situations. Taking into consideration the advantages of the fuzzy systems such as simplicity, ease of application, flexibility, speed and ability to deal with imprecision and uncertainties. This factor can be useful for power quality evaluation, cost-effective analysis of PQ mitigation techniques and as well as billing purposes in these situations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".