Real-Time Anomaly Detection in Distribution Grids Using Long Short Term Memory Network
Bibliographic record
Abstract
The massive amount of data generated by smart meters provides opportunities to better monitor and control power grids in real-time. However, making use of such enormous amounts of data can be a challenge. Sensor measurements collected in power systems can be anomalous, reflecting sensor malfunctions, power system disturbances, or any other problems that may cause abnormal readings. With the rapid deployment of distributed energy resources, traditional methods for protecting the grid, which relies on emergency load tripping through relay actions, have limited performance. For example, relay actions may be delayed. In addition, they cannot detect anomalies that are within the required voltage range. To address these shortcomings, this article proposes a data-driven framework based on a long-short-term memory network model to directly detect anomalies in distribution systems using voltage magnitude measurements. The proposed solution is validated against known anomalies using data from a real distribution grid. The simulation results show highly accurate anomaly identification.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".