Strategy and Practice of Power System Relay Protection under Extreme Weather Conditions
Bibliographic record
Abstract
With the continuous expansion and increasing complexity of the power system, the protection requirements for the power system are also increasing. Although traditional relay protection systems can play a certain protective role, they have some limitations, such as the inability to comprehensively monitor the power system and the lack of accurate judgment. Developing and applying intelligent relay protection systems has become an important way to improve the safety and reliability of power systems. This article explored the relay protection strategies and practices for power systems under extreme weather conditions. Traditional relay protection systems have limitations in addressing the increasingly complex protection needs of power systems. Therefore, the development and application of intelligent relay protection systems have become a key way to improve the safety and reliability of power systems. This article verified the effectiveness of the knowledge base based relay protection fault handling process in improving the safety, stability, and fault handling efficiency of power systems through experimental results and discussions. The experimental results showed that under different fault conditions, the processing accuracy was generally high, and in the vast majority of cases, the accuracy exceeded 90%. For example, the accuracy of handling line short circuits and voltage anomalies was 95% and 96%, respectively.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".