Real-time detection of short-circuit faults in power systems using LSTM autoencoder
Bibliographic record
Abstract
This paper presents a real-time detection method for short-circuit faults in meshed power networks, based on a Long Short-Term Memory (LSTM) autoencoder. The objective is to quickly and accurately identify both simple faults (single-phase, double-phase, three-phase), hybrid faults, and triple faults characterized by the simultaneous occurrence of several types of faults on different network lines. Simulations were carried out on an IEEE 9-bus system modeled in MATLAB/Simulink, covering various scenarios: faults on a single line, on two lines, and on three lines simultaneously. The results obtained show an immediate reaction of the model to disturbances, reflected by a marked increase in the loss function at the time of the incident. The system also demonstrated its ability to detect the indirect impact of faults on neighboring lines, reflecting the propagation of electrical imbalances throughout the network. These performances confirm the effectiveness and robustness of the LSTM autoencoder for intelligent, fine, and adaptive monitoring of modern power networks, fully meeting the requirements of real-time protection systems.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".