Out-of-step Detection Using Energy Equilibrium Criterion in Time Domain
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This article introduces a new algorithm to detect the out-of-step condition in a power system based on energy equilibrium criterion in the time domain. The proposed energy equilibrium criterion is developed using the concept of equal area criterion in the power-angle domain, and it eliminates the numerical computations required to find the critical clearing time to detect the out-of-step condition. The proposed algorithm detects the out-of-step condition based on the real-time transient energy information available from the local substations. The effectiveness of the proposed algorithm is tested on a single-machine infinite-bus system, a two-machine infinite-bus system, and a three-machine infinite-bus system. The performance of the proposed algorithm is compared with an existing concentric rectangle scheme. The simulation results show that the proposed algorithm can be applied to larger systems and is faster compared to the concentric rectangle scheme.
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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 | 0.000 | 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 it