A Machine Learning-Based Framework for Fast Prediction of Wide-Area Remedial Control Actions in Interconnected Power Systems
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Bibliographic record
Abstract
This paper presents a novel real-time machinelearning-based framework for remedial control action (RCA) prediction to prevent transient instability in interconnected power systems. Due to the fast dynamics of rotor angle oscillations and considering communication latencies, there is limited time to compute RCA, making RCA calculation impractical in events quickly evolving into transient instability. The proposed algorithm predicts RCA based on pre-fault and post-fault voltage values of generator buses. To cover credible scenarios, reduce prediction complexities, and increase accuracy, a micro model strategy is employed in which independent models are built for each transmission line of the system. The proposed framework consists of three main modules: stability prediction, coherency prediction, and RCA prediction. In the coherency prediction module, a time-varying algorithm is developed that determines the optimal number of generator groups and prevents unnecessarily overestimated RCAs. For each of the considered scenarios and obtained coherency patterns, a mixed-integer linear programming (MILP) model is utilized to extract the islanding and load shedding patterns considering transient stability constraints. The effectiveness of the proposed approach is demonstrated on the IEEE 39-bus system and the 74-bus Nordic test system, followed by a discussion of results.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it