A robust technique for overvoltages classification in power transformers
Bibliographic record
Abstract
A fast and reliable technique for classifying overvoltage events across power transformers terminals is introduced in this paper. Currents at the secondary terminals of power transformer during various overvoltages are used to synthesis a modal current signal. A feature vector is extracted from the selected modal signal utilizing discrete wavelet transform. Finally the extracted feature vector is used to train an artificial neural network to differentiate between various overvoltage events occurring across the transformer premises. The results of this algorithm can be used to build an online model to help assessing the condition of power transformers, thus proper condition based maintenance can be scheduled. The proposed algorithm is also fast in the sense that it can differentiate between temporary and permanent overvoltages during the early transient stage, thus eliminating the need for any time delay in the overvoltage protection devices, and the overvoltage protection philosophy can be changed to become instantaneous rather than time-delayed protection. This technique is economical and simple; it doesn't require any special arrangements as it depends on the readily available measurements. Tests were conducted to validate the proposed algorithm and showed it to be robust and generic.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".