A Hybrid Method for Fast Rotor-Angle Stability Assessment
Bibliographic record
Abstract
This article proposes a novel hybrid time-domain and direct stability method for rotor-angle stability assessment, aiming to improve the efficiency of existing approaches. The proposed method enables faster detection of both small-signal and transient stability scenarios while extending the applications of the classical stability direct methods to multiswing stability analysis. Unlike the conventional direct methods that rely on the overall system energy, the proposed approach calculates the system's critical energy using the critical apparatus energies, facilitating multiswing stability analysis. Key contributions of this work include the introduction of a new metric, termed “the time to instability,” which allows for the prediction of separation or islanding areas during disturbances. Additionally, the proposed method can rank all apparatus in a power system based on their criticality during small or large disturbances. Also, a stopping condition for the time-domain simulation is provided, reducing algorithm execution time and rendering it suitable for real-time or near-real-time application of dynamic security assessment. The proposed method is tested with multiple stability scenarios and the four possible stability scenarios are presented in this article using the IEEE 16-machine 68-bus power system. The results demonstrate the high accuracy of the proposed approach in identifying the critical apparatus and assessing first- and multirotor-anglestability in 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.005 | 0.002 |
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".