A Novel Automatic Detection Model for Single Line-to-ground Fault
Bibliographic record
Abstract
The single line-to-ground (SLtG) fault is difficult to locate or troubleshoot rapidly by traditional methods, posing a serious threat to the safety and stability of the power system.Since the transient negative sequence current (NSC) is immune to the arc-suppression coil of the grid, this paper sets up an automatic detection model for the SLtG fault in the grid.Firstly, the positive sequence fault current and negative sequence fault current were extracted from the transient process current of the grid, and combined into the transient NSC.After that, the characteristic region of the NSC was determined by the change of the transient NSC.To further define this region, the matrix algorithm was introduced to extract the exact feature points of the fault region.Taking the feature points as the input vectors, the neural network (NN) was adopted to identify the fault position.Simulation results show that our model achieved a 4 % lower false acceptance rate (FAR), a 10 % lower false rejection rate (FRR), and a much higher efficiency than the traditional detection model.The research findings lay the basis for fault data analysis and online fault diagnosis and improve the reliability of grid operations.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 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".