Intelligent Relay Based on Artificial Neural Networks ANN for Transmission Line
Bibliographic record
Abstract
The detection of faults on transmission lines is an essential and important part of power system monitoring and control. Providing high-quality electric power requires an efficient, reliable, and intelligent protection, a system that can handle transmission line outages that result from a variety of random reasons. This system will allow a fast detection and gives an accurate fault location, thus isolating the faulted section and avoiding catastrophic damage to material and human assets.In this paper, the use of artificial neural network algorithm ANN is proposed, which can be implemented in a numerical relay, this approach has been noticed by many researchers in the field of power system protection. ANN is trained using the measurements of the three-phase currents and voltages. The feedforward neural network was used together with the backpropagation algorithm to detect, classify, and localize the fault. To validate the choice of the neural network, a detailed analysis was performed with a different number of hidden layers. Simulation results show that the present artificial neural network-based method performs satisfactorily in detecting, classifying, and locating faults on transmission lines. To test the proposed method, different fault scenarios were simulated
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".