Determining the Harmonic Impacts of Multiple Harmonic-Producing Loads
Why this work is in the frame
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Bibliographic record
Abstract
Identifying harmonic sources in a given power system is an important task for utility power-quality (PQ) management. This paper presents a new class of harmonic source identification problems: how to quantify the harmonic impact of several known harmonic-producing loads on the harmonic levels observed at a network location. This paper first defines the problem and proposes a harmonic impact index to theorize the problem. This paper then presents a statistical-inference-based method to estimate the index. The data required for this analysis are the harmonic voltage and current magnitudes continuously collected by the existing PQ monitors. The characteristics of the proposed method are investigated through case studies. Finally, additional applications and improvements of the proposed method are discussed.
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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 | 0.000 |
| 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 it