Intelligent fault locator and zone isolation for transmission line
Bibliographic record
Abstract
Power transmission systems play an important role in modern society. When transmission system outages occur, fast and proper restoration is critical to improving service quality and customer satisfaction. To ensure high power quality, an intelligent and reliable protection system is required. This system must be able to handle faults in transmission system that occur for a various of random causes; it must also grant for rapid detection and precise location of the fault, isolation of the faulty section, and prevention of devastating damage to people and equipment. In this article, the ANN (Artificial N3eural Network) is proposed as a mechanism for fault detection, classification, and localization. This new approach can be trained by measuring three-phase currents and voltages and implemented in a relay to make it intelligent. Therefore, in case of a fault, the proposed intelligent relay can locate the fault and send a tripping command to the circuit breakers to disconnect the faulty zone from the whole structure. Different fault scenarios are considered to test the tripping measures.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".