ADEQUACY ASSESSMENT OF COMPOSITE POWER SYSTEMS INCORPORATING FACTS
Bibliographic record
Abstract
The rapid development of flexible AC transmission technology and its immense potential for future use dictate the need to seriously consider the associated reliability benefits that can be obtained. The application of flexible AC transmission system (FACTS) in modem power systems, particularly in restructured power industries, has received considerable attention in recent years. This is due to the potential reliability, economic and environmental benefits from using this new technology to avoid building new transmission lines. It is, therefore, both necessary and important to develop reliability evaluation techniques to assess the actual benefit obtained from utilizing FACTS devices in a bulk power system. This thesis describes the development of appropriate models and techniques to permit quantitative reliability evaluation of composite generation and transmission systems incorporating FACTS devices. The analyses are conducted using the contingency enumeration approach: The FACTS transmission unit is represented by a multi-state model. Two network evaluation techniques, the reinforced minimal tie set method and the modified DC load flow method are utilized to assess the impact of FACTS devices in composite power system adequacy. The developed models and techniques can be used to conduct adequacy studies on a wide range of composite power systems. The utilization of these techniques is illustrated by application to two widely used reliability test systems. The results and discussions presented in this thesis should provide valuable information to electric power utilities engaged in planning and operating FACTS devices.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".