Data-Driven Based Transmission Line SLG Fault Prediction of Fault Location and Resistance in PLL-Synchronized Inverter-Based Resources
Bibliographic record
Abstract
Inverter-based resources (IBRs) have been widely integrated into the grid for utilizing renewable energy. However, during grid faults, IBRs are more prone to instability and have limited capability to support the grid due to their weak overcurrent capacity. To ensure both grid stability and support under limited overcurrent capability, it is necessary to quickly identify the fault type and fault location and then accordingly adjust the output power of IBRs. Considering single-line-to-ground (SLG) faults are most likely to occur among various transmission line faults, this paper aims to accurately and quickly predict the fault location and fault resistance in the transmission line for the PLL-synchronized IBRs. Finally, based on the multi-layer perceptron (MLP) neural network framework, a prediction model is trained and tested, and results confirm both its efficiency and accuracy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".