Secure and scalable power system event identification with renewable integration via federated LSTM and adaptive privacy mechanisms
Bibliographic record
Abstract
The power system event detection process must accelerate and become more precise with increasing penetration of renewable energy systems into the grid. The authors discuss a federated learning LSTM (FL-LSTM) that serves as a secure detection system for distributed grid operations, keeping user data private. The federated model achieves better cross-grid generalization through the integration of system physical constraints, which operate under a physics-guided loss function that unites swing-equation consistency with ROCOF limits and frequency nadir bounds. To address privacy and noise sensitivity, we implement an adaptive differential privacy mechanism that modulates Gaussian noise per event stream based on event frequency, maintaining a global ( ɛ , δ )-DP budget while preserving rare-event sensitivity. This facilitates improved event detection without compromising data privacy. Simulations on the modified IEEE 39-bus system with varying renewable levels show that, compared to benchmark LSTM using central SCADA/DC data, the federated model converges faster, identifies events more accurately, and requires less communication. It preserves its distributed nature, stays robust to unseen events, and proves to be a strong candidate for privacy-preserving event detection in renewable-rich power systems. • FL-LSTM enables secure event detection across distributed power grids. • Physics-guided regularization boosts cross-grid model compatibility. • Adaptive DP balances privacy and accuracy based on event frequency. • Outperforms centralized LSTM in accuracy and communication efficiency. • Robust to unknown events in renewable-rich power 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".