Survivability Analysis of Hybrid Micro-Grid Systems
Bibliographic record
Abstract
Hybrid Micro-grid systems have been developed as flexible and reliable platforms for integrating dc and ac distributed energy resources (DERs) to supply different load types. The structure and components of a hybrid micro-grid system makes it vulnerable to various types of dynamic events, which can take place in its dc and/or ac components. Such dynamic events can present major challenges for the stability and functionality of a hybrid micro-grid system. This paper presents the survivability analysis as a tool to model the impacts of dynamic events on a hybrid micro-grid system. The presented tool is based on the use of a survivability index Γ that is defined in terms of the difference in the bus power injections before and after a dynamic event. The index Γ has boundary values to differentiate between survivable and non-survivable dynamic events in a hybrid micro-grid system. The survivability analysis is implemented and tested for a hybrid micro-grid system under various types of dynamic events. Test results demonstrate the ability of the survivability analysis to accurately model and quantify impacts of dynamic events on a hybrid micro-grid system.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".