Assessing the Collective Harmonic Impact of Modern Residential Loads—Part I: Methodology
Bibliographic record
Abstract
The proliferation of power-electronic-based residential loads has resulted in significant harmonic distortion in the voltages and currents of residential distribution systems. There is an urgent need for techniques that can determine the collective harmonic impact of these modern residential loads. These techniques can be used, for example, to predict the harmonic effects of mass adoption of compact fluorescent lights. In response to the need, this paper proposes a bottom-up, probabilistic harmonic assessment technique for residential feeders. The method models the random harmonic injections of residential loads by simulating their random operating states. This is performed by determining the switching-on probability of a residential load based on the load research results. The result is a randomly varying harmonic equivalent circuit representing a residential house. By combining multiple residential houses supplied with a service transformer, a probabilistic model for service transformers is also derived. Measurement results have confirmed the validity of the proposed technique. The proposed model is ideally suited for studying the consequences of consumer behavior or regulatory policy changes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.001 |
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".