A new fuzzy-wavelet based representative quality power factor for stationary and nonstationary power quality disturbances
Bibliographic record
Abstract
Power factor is an important index for evaluating the transmission efficiency in electric power system (EPS) along with the quality of the transmitted power. Three different power factors currently exist in the standards to satisfy those requirements namely; displacement, transmission efficiency and oscillation power factors. Currently those three power factors are defined according to the fast Fourier transform (FFT) which produces inaccurate results in case of nonstationary (sinusoidal or nonsinusoidal) waveforms. Therefore in this paper, the three power factors are redefined in the time-frequency domain using the wavelet packet transform (WPT) that proves to be capable of accurately measuring and representing EPS waveforms especially under nonstationary disturbances. Then a new fuzzy-wavelet based representative quality power factor module is developed to amalgamate the three redefined power factors into one single index called fuzzy-wavelet representative quality power factor (FWRQPF). The advantage of the new index is to evaluate quantitatively, qualitatively and accurately the power transmission efficiency while benefiting from the advantages of fuzzy systems to be simple, easy to be used with no need for an expert since it contains its own knowledge base. The new index can be indicative for billing purposes, evaluating the PQ in deregulated markets, help deciding suitable PQ mitigation and power factor correction techniques for power factor improvements under stationary or nonstationary operating conditions.
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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) | 0.000 | 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".