Recognition of fault transients using a probabilistic neural-network classifier
Why this work is in the frame
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Bibliographic record
Abstract
Summary form only given. This paper investigates the applicability of decision tree, hidden Markov model, and probabilistic neural-network(PNN) classification techniques to distinguish the transients originating from the faults from those originating from normal switching events. Current waveforms due to different types of events, such as faults, load switching, and capacitor bank switching were generated using a high-voltage transmission system simulated in PSCAD/EMTDC simulation software. Simulated transients were used to train and test the classifiers offline. The wavelet energies calculated using three-phase currents were used as input features for the classifiers. The results of the study showed the potential for developing a highly reliable transient classification system using the PNN technique. An online classification model for PNN was fully implemented in PSCAD/EMTDC. This model was extensively tested under different scenarios. The effects of the fault impedance, signal noise, current-transformer saturation, and arcing faults were investigated. Finally, the operation of the classifier was verified using actual recorded waveforms obtained from a high-voltage transmission system.
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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 | 0.000 | 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 it