Accurate harmonic source identification using S-transform
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Bibliographic record
Abstract
This paper introduces the accurate identification of harmonic sources in the power distribution system using time-frequency distribution (TFD) analysis, which is S-transform. The S-transform is a very applicable method to represent signals parameters in time-frequency representation (TFR) such as TFR impedance ( Z TFR ) and the main advantages of S-transform it can provide better frequency resolution for low frequency components and also offers better time resolution for high-frequency components. The identification of multiple harmonic sources are based on the significant relationship of spectral impedances ( Z S ) that extracted from the Z TFR , consist of the fundamental impedance ( Z 1 ) and harmonic impedance ( Z h ). To verify the accuracy of the proposed method, MATLAB simulations carried out several unique cases on IEEE 4-bus test feeder cases. It is proven that the proposed method is superior, with 100% correct identification of harmonic source location. It is proven that the method is accurate, fast and cost-efficient to localize harmonic sources in the power distribution system.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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