Practical Power Quality Charts for Motor Starting Assessment
Why this work is in the frame
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Bibliographic record
Abstract
The impact of motor starting on power quality can be assessed using detailed computer simulation studies. However, not every motor installation case needs such an extensive assessment. Utility planners are interested in quick evaluation of the potential impact of a motor installation proposal. Based on the findings, they can then determine if detailed case studies and what types of case studies are necessary. This paper presents three charts for motor starting planning according to three power quality concerns. These concerns are the amount of voltage drop caused by motor starting, the compliance to the ITIC curve, and the compliance to the IEC flicker meter limits. These charts can help utility planers to conduct quick and first-cut assessment of a motor starting situation. They also reveal the key factors affecting the motor starting related power quality concerns. The principles behind these charts are explained. Examples are given to show how to use them for quick assessment of motor starting impact.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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