Information extraction from PQ disturbances — An emerging direction of power quality research
Bibliographic record
Abstract
Power quality is a field that deals with all sorts of power disturbances. Past power quality research and development activities have been focused on the harmful aspects of the disturbances. With the wide spread use of power quality monitoring tools, more and more users and developers start to realize that power disturbances can carry valuable information about the conditions of a system and its equipment. As a result, initiatives that explore the “useful” aspects of power disturbances have emerged. For example, short-circuit caused voltage sag disturbances have been exploited for fault location purposes, characteristics of capacitor-switching transients are used to determine and locate which feeder capacitors are operating normally. Such information-oriented use of power disturbance data and monitoring techniques could emerge as an important field of the future smart grid. The objective of this paper is to present some of the example developments in this interesting field. We hope this survey/review paper will help to stir up broader research interests in the field and serves as a step stone for PQ disturbance researchers.
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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 | 0.003 |
| 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".