New high precision harmonic analysis method for power quality assessment
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".