Artificial Intelligence Based Fault Diagnosis and Relay Protection Technology in Power Systems
Bibliographic record
Abstract
Nowadays, existing fault diagnosis technologies have problems such as slow response speed, low accuracy, and weak adaptive ability. To prevent overfitting, this article can use a strictly separated set of training and testing samples to train the model. In order to ensure the generalization performance of the model, mutual confirmation technology was adopted. The computing power of GPUs can be utilized to effectively process massive amounts of data and improve training efficiency. In the field of fault diagnosis, the proposed method can achieve real-time collection of the operating status of the power grid, and use the established artificial intelligence model to analyze it, thereby achieving rapid identification and localization of system fault types and locations. This method has self-learning function, which can continuously improve the accuracy of fault diagnosis while accumulating data. At the same time, the algorithm also has an alarm function, which can predict and warn the system before it malfunctions, thereby taking corresponding preventive measures. At a transmission speed of 10 kbps, the error detection accuracy of the system reached 98.5%. This article can promote the development of power grid fault diagnosis and protection technology, which is conducive to providing new ideas and methods for power system fault diagnosis and relay protection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".