Data segmentation algorithms for a time-domain harmonic source modeling method
Bibliographic record
Abstract
Nowadays, with a fast increasing in renewable energy applications and vast deployment of plug-in electric vehicle, power quality issue becomes more important than before because of considerable harmonic current injected into grid. To better manage these distributed energy sources/loads and reduce harmonic pollution, establishing more accurate harmonic source models for them becomes very necessary. Traditionally, harmonic source is described as a fixed-parameter model, which is not good enough to describe time-varying harmonic source. In this paper, a time-domain modeling method is firstly explained, which enables the study on time-varying harmonic source model. Then two different data segmentation algorithms to support this model based on the stability of current magnitude and phase angle are developed and explained respectively using a group of data acquired from field. One is developed from statistical perspective while the other is based on slope of curve. Comparison is presented to explain their unique characteristics and synthesizing of both algorithms is also illustrated. The results of algorithms are shown to be satisfactory.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".