Development and hardware implementation of a fault transients recognition system
Bibliographic record
Abstract
Summary form only given. This paper presents the development and hardware implementation of a classification scheme to distinguish the transients originated by faults from the other types of transients. In the proposed scheme, a set of Hidden Markov Model (HMM) based classifiers are employed to recognize the fault transients. Input features for the classifiers are the energy contained in wavelet coefficients of the measured current waveforms. A laboratory prototype of the fault recognition system was implemented on a floating point Digital Signal Processor (DSP) based hardware platform. The classification system was tested using the transient signals generated by a real-time waveform playback unit. The test waveforms were generated by simulating an actual extra high voltage (EHV) transmission system on an electromagnetic transient (EMT) simulation program. Operation of the classification system was further verified using waveforms obtained from an actual fault recorder. The performance of the classifier was investigated under different practical scenarios such as current transformer saturation, measurement noise and lightning faults.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".