New high precision harmonic analysis method for power quality assessment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The power quality is one of actual major problems in electrical engineering. Generally, power electronics equipments damage power quality parameters, disturbing radio communications or the functionality of other equipments. A rigorous design of most appropriate filters for power quality improvement is possible only through a high precision analysis that allows estimating power quality parameters and the influence of each harmonic component on the network-drive system. Actual industrial equipments intended to perform spectral analysis are not appropriate for strongly deformed signals, with frequent discontinuities, as in power electronics. Our paper presents a new and accurate method of harmonic analysis that permits to mitigate most of power quality related problems. The principle is to estimate intermediate points between the initial samples given by the available data acquisition system; therefore, the Fourier coefficients are estimated more precisely using the fast Fourier transform. As interpolation technique we chose the reconstruction of the analog signal using an ideal lowpass filter. The excellent results are validated on a pair of synthesized signals having known harmonic spectrum
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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 | 0.001 |
| 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 it