Research on Grid Wide Area Protection Scheme Based on Data Fusion and Distributed Computing
Bibliographic record
Abstract
Wide-area protection systems are capable of eliminating or mitigating the consequences of disturbances by obtaining multi-point information about the grid system through measurement and communication techniques, and power system control and protection systems.In this study, distribution data in the grid system is collected and preprocessed, the distribution state of the grid system is estimated using data fusion methods, and an optimization method for distribution state estimation based on distributed computing methods is proposed.Then the grid wide-area protection system is designed by combining the grid system fault diagnosis method.Simulation and example analysis results show that the grid wide-area protection system based on data fusion and distributed computing has good performance in processing grid data and detecting and localizing grid faults, and the maximum localization error of faulted line points is maintained within 0.770%.In addition, this grid wide-area protection system is able to accurately detect a certain circuit fault in a regional grid system where faults are frequent, avoiding large-scale power outages and ensuring the stable operation of the grid system.This study has important scientific significance and application value for grid multivariate data fusion modeling and real-time fault detection, and provides an effective widearea protection scheme for the grid system.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".