Reformulating Three-Phase Power Components Definitions Contained in the IEEE Standard 1459-2000 Using Discrete Wavelet Transform
Bibliographic record
Abstract
Summary form only given. Power components definitions contained in the IEEE Standard 1459-2000 for unbalanced three-phase systems with non-sinusoidal situations are represented in the frequency domain based on Fourier Transform (FT). However, FT suffers from the high computational effort especially when the number of phases increases and it is unable to provide information concerning time content because it provides only an amplitude-frequency spectrum. On the other hand, the Discrete Wavelet Transform preserves both time and frequency information while reducing the computational effort through dividing the frequency spectrum into bands and thus overcomes the limitations of FT. In this paper the three-phase power components definitions contained in the IEEE Standard 1459-2000 are reformulated using the DWT and thus redefined in the time-frequency domain. Also in order to study system unbalance, the concept of symmetrical components is defined in the wavelet domain. The results obtained from applying the IEEE Standard definitions and the DWT based definitions to balanced and unbalanced three-phase systems under non-sinusoidal operating conditions, indicate that the DWT based definitions are very accurate and the problem of spectral leakages can be reduced by suitable choice of the mother wavelet and the wavelet family. The DWT based definitions are useful in studying non-stationary waveforms.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".