ANN-based technique for fault location estimation using TLS-ESPRIT
Bibliographic record
Abstract
In this paper, a methodology is proposed for identifying the fault location in transmission lines. The required features for the proposed algorithm is extracted from transient currents or voltages waveforms measured at the substation using the total least square-estimation of signal parameters via rotational invariance technique (TLS-ESPRIT). Since these transient waveforms are considered as a summation of damped sinusoids, TLS-ESPRIT is used to estimate different signal parameters mainly damping factors, frequencies and amplitudes of different modes contained in the signal. These parameters are functions of the fault location, system damping and fault time. Those features can then be employed for fault location identification. Artificial neural networks (ANNs) are used to estimate the fault location using this modal information. Training and testing data are generated using PSCAD/EMTDC simulations. It is crucial to indicate that no pre-fault data is required in this algorithm.
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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 | 0.000 | 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 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".