A hybrid machine learning approach for real-time reliability evaluation of power systems
Bibliographic record
Abstract
Abstract Reliable operation of modern power systems requires accurate state evaluation and efficient load management under dynamic and uncertain conditions. This study presents an AI-enhanced hybrid optimization framework that integrates DC power flow modeling, mixed-integer linear programming (MILP), and a Transformer-based architecture to dynamically optimize generator dispatch and key reliability metrics, including Loss of Load Probability (LOLP), Expected Energy Not Supplied (EENS), and Loss of Load Frequency (LOLF). The framework incorporates a self-attention mechanism to enhance stability prediction and support the integration of renewable energy sources. The proposed framework demonstrates superior performance on the IEEE RTS-96 and Saskatchewan Power Corporation (SPC) systems, achieving 93.7% prediction accuracy with the lowest RMSE and MAE among all tested models. It outperforms benchmark models such as Convolutional Neural Networks (CNN), Convolutional XGBoost (ConXGB), Convolutional Random Forest (ConRF), Physics-Informed Neural Networks (PINN), and Graph Neural Networks (GNN), while also reducing computational time by 60.5%, confirming its efficiency and suitability for real-time reliability assessment. Additionally, the proposed approach improves cost-reliability trade-offs in load curtailment decisions, offering a scalable and adaptive solution for modern power system reliability analysis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".