Investigation of the applicability of Lyapunov Exponents for transient stability assessment
Bibliographic record
Abstract
Lyapunov Exponents (LEs) measure the exponential rates of divergence or convergence of nearby trajectories in state-space models. The study presented in this paper investigates the applicability of this concept to power system transient stability analysis. It is shown that the Largest Lyapunov Exponent (LLE) of the post-fault system subsequent to a given fault scenario is independent of the fault clearing time up to the Critical Clearing Time (CCT). Furthermore, the set of exponentially stable equilibrium points of the post-fault system corresponding to different load-generation combinations of the pre-fault system can also be characterized by this LLE. The study compares the LEs with the conventional Time Domain Simulation (TDS), Extended Equal Area Criterion (EEAC) and Potential Energy Boundary Surface (PEBS) methods. It is shown that LEs give reliable results. Further, the power system stability regions can be determined using the invariance of LEs within the same stability region.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".