Computer simulation of transient stability analysis of power systems.
Bibliographic record
Abstract
Determination of stability characteristics of a power system is considered as a substantial issue with the increasing complexity of power systems. Power system stability is defined as the behavior of the electrical power system under sudden or sequence of disturbances when it is operating in steady state. It could be a problem of voltage stability, frequency stability or rotor angle stability depending of the type of disturbance. Transient stability which is a sub section of rotor angle stability, is concerned with the condition in which the synchronous machines in the system remain in synchronism or 'in-step' with each other when the system is subjected to severe disturbances. In multi-machine power system, transient stability analysis is an indispensable tool in the areas of planning, design, operation and research. The nonlinear nature under disturbance is not linearised for the purpose of analyzing transient stability. Therefore, the theories and methodologies for transient stability analysis based on approximations and assumptions. This study is to prepare an educational software package to understand the transient stability behavior while understanding the theories and methodologies behind it. (Abstract shortened by UMI.)Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .J39. Source: Masters Abstracts International, Volume: 43-01, page: 0276. Adviser: G. R. G. Raju. Thesis (M.A.Sc.)--University of Windsor (Canada), 2004.
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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 | 0.003 |
| 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) | 0.015 | 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".